Autonomous Swarm Orchestration With Offline Strategy Precomputation

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Solution Overview

Problem

Current methods for real-time autonomous swarm conduct and orchestration face challenges such as complex computation under uncertainty, high computational demands, sensitivity to swarm size, and inability to handle multiple swarms with different objectives, particularly in non-deterministic environments and parallel operations.

Innovation Solution

An autonomous system comprising an offline model-based subsystem for precomputation and a real-time subsystem for intercommunication, utilizing modules like communication modules, timely ingestions and verifications, precomputation solvers, swarm runners, and optimizers to process and execute strategies efficiently across multiple swarms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If complex computation is performed for decision making under uncertainty in real-time, then decision quality improves, but computational time exceeds real-time requirements

Engineering Contradiction:

Improvedecision qualityVSAvoidcomputational time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs precomputation of swarm strategies offline before real-time operation. The offline subsystem computes complex decision-making models and strategies in advance, storing them for rapid retrieval and execution during real-time operations, thus avoiding time-consuming computations during critical moments

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The computational system is divided into two independent subsystems: an offline subsystem for complex precomputation and a real-time subsystem for rapid execution. This segmentation allows each subsystem to be optimized for its specific function, with the offline subsystem handling intensive computations and the real-time subsystem handling time-sensitive operations

Inventive Principle:

Principle #1Segmentation

2Power

If more computing resources are allocated for swarm orchestration, then computational capability improves, but availability of computing resources online decreases

Engineering Contradiction:

Improvecomputational capabilityVSAvoidavailability online

Core Design Contradiction:

PowerVSEase of operation

Solution Approach 1:

Computational resources are utilized offline to precompute swarm strategies before deployment. By performing computations when resources are available offline, the system avoids the constraint of limited online computing resources while still achieving high computational capability

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a computational model copy that operates offline to generate strategies, which are then used by the real-time system. This allows the full computational power to be applied in creating the model without requiring the same resources during real-time operation

Inventive Principle:

Principle #26Copying

3Reliability

If existing swarm conduct methods are used, then small swarm coordination works, but sensitivity to swarm size increases and scalability deteriorates

Engineering Contradiction:

Improvecoordination effectivenessVSAvoidswarm size scalability

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The offline precomputation subsystem generates universal swarm strategies that can adapt to different swarm sizes and configurations. The system is designed to handle varying swarm scales by computing strategies that are applicable across different scenarios, making the solution scalable from small to large swarms

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system adjusts computational parameters and strategy complexity based on swarm size. The offline subsystem can compute strategies tailored to specific swarm configurations, and the real-time subsystem executes these strategies with parameters optimized for the actual swarm scale being operated

Inventive Principle:

Principle #35Parameter changes

4Reliability

If coordinated swarm conduct is implemented, then single swarm optimization improves, but ability to conduct multiple swarms in parallel deteriorates

Engineering Contradiction:

Improvesingle swarm optimizationVSAvoidparallel swarm conduct capability

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system segments swarm management into independent orchestrator instances, each capable of managing a specific swarm. The offline subsystem can precompute strategies for multiple swarms simultaneously, and the real-time subsystem can execute multiple swarm operations in parallel without interference

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The offline precomputation subsystem acts as an intermediary that prepares strategies for multiple swarms before real-time operation. This intermediary layer allows complex multi-swarm coordination to be resolved offline, enabling the real-time system to efficiently manage multiple swarms in parallel with reduced computational burden

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12158766B2Real-time autonomous swarms conduct and orchestration

Publication Date: 2024.12.03 NAVIGATE LTD

AI summary

The present invention discloses systems, modules and methods for an autonomous orchestrion of at least one first swarm, comprising offline model-based subsystem for precomputation of swarm strategies to be performed at real-time; and a real-time subsystem intercommunicated with the offline pre-commutating subsystem.